As a Data Scientist at Safaricom, you sit at the heart of East Africa’s digital transformation. Safaricom is not just a telecommunications company; it is a massive technology ecosystem driven by M-PESA, financial services, enterprise solutions, and high-speed connectivity. In this role, you will be responsible for translating petabytes of transactional, network, and customer data into actionable models that directly impact over 40 million subscribers.

The work you do has immediate, real-world consequences. Whether you are building credit scoring algorithms for financial inclusion products like Fuliza and M-Shwari, optimizing network traffic to prevent outages, or designing hyper-personalized customer campaigns, your models will run at an unprecedented scale. You will collaborate closely with product managers, software engineers, and business leaders to turn raw data into strategic assets.

This role requires a rare blend of deep technical expertise and strong business acumen. You must be comfortable navigating complex, sometimes ambiguous data landscapes while maintaining a clear focus on delivering value to the end user. It is a challenging but highly rewarding position where your insights can shape the economic landscape of the entire region.

The questions you will face during the Safaricom hiring process are designed to evaluate your technical execution, system design capabilities, and behavioral alignment with the company’s customer-first values. These questions are drawn from real candidate experiences and reflect the actual challenges you will encounter on the job.

Coding and SQL (Codility Assessment)

This category tests your foundational programming efficiency, algorithmic thinking, and ability to manipulate large datasets using SQL and Python.

• Write a SQL query to calculate the month-over-month growth rate of active M-PESA users.
• Given an array of integers representing network latency logs, write an efficient Python algorithm to find the longest contiguous sub-segment of stable latency.

Preparing for a Data Scientist interview at Safaricom requires a structured approach that balances technical mastery with communication skills. You should focus on demonstrating not just your ability to write code, but your capacity to solve real-world business problems.

Technical and Analytical Proficiency – You must demonstrate a strong command of Python, SQL, and core machine learning concepts. Your interviewers will look for clean, optimized code and a deep understanding of statistical modeling, feature engineering, and evaluation metrics.

Problem-Solving and Solution Design – Safaricom values candidates who can take ambiguous business problems and translate them into structured data science workflows. Be prepared to explain your methodology, justify your choice of algorithms, and describe how you would scale your solutions.

Leadership and Stakeholder Communication – As a Data Scientist, you will interact with various departments, from marketing to finance. You must be able to articulate the business value of your models, build trust with cross-functional teams, and show leadership when driving projects forward.

Culture and Values Alignment – Safaricom places a premium on integrity, customer obsession, and collaboration. You should be ready to share examples of how you have demonstrated these values in your previous roles, showing that you are a team player who prioritizes the collective success of the organization.

The interview process for a Data Scientist at Safaricom is structured to evaluate both your technical execution and your collaborative potential. It typically begins with an online application through the company's career portal, followed by a multi-stage evaluation process designed to test different facets of your skills.

The first critical hurdle is a timed technical assessment, usually administered via platforms like Codility or HackerRank. This test focuses heavily on Python programming and SQL query optimization. Candidates who pass the technical screen are then invited to a series of interviews, which may include a behavioral screening with HR and a comprehensive technical and managerial panel interview led by the Head of Department.

The timeline above illustrates the standard progression from your initial application to the final offer stage. Candidates should use this timeline to pace their preparation, ensuring they allocate sufficient time to practice coding challenges before the initial assessment and mock interviews before the panel stage. While the process is rigorous, it is designed to be conversational and collaborative, allowing you to showcase your unique strengths.

To succeed in the Safaricom interview process, you must understand exactly what the hiring team is looking for in each key competency area.

SQL and Algorithmic Coding

This area evaluates your ability to write clean, efficient, and scalable code. Because Safaricom processes massive volumes of data daily, poorly written queries or inefficient algorithms can lead to significant performance bottlenecks.

Be ready to go over:

• SQL Window Functions – Mastery of aggregation, ranking, and analytical functions over specific partitions.
• Data Structures – Efficient use of lists, dictionaries, and sets in Python to solve algorithmic problems.
• Query Optimization – Understanding indexing, execution plans, and minimizing resource usage in database queries.
• Advanced concepts (less common) – Graph algorithms for network analysis, parallel processing techniques in Python, and custom database user-defined functions (UDFs).

Example questions or scenarios:

• "Optimize a query that calculates the cumulative transaction volume for every customer over a rolling 30-day window."
• "Write an algorithm to detect anomalous transaction patterns in real-time streaming data."

Machine Learning and System Design

This evaluation area focuses on your ability to design end-to-end machine learning systems that can operate reliably in a production environment.

Be ready to go over:

• Feature Engineering – Creating meaningful features from raw transactional and network logs.
• Model Evaluation Metrics – Choosing the right metrics (e.g., Precision-Recall, ROC-AUC, F1-score) based on business objectives.
• Scalability and Deployment – Designing pipelines that can handle high-throughput inference with low latency.
• Advanced concepts (less common) – Deep learning for unstructured data, reinforcement learning for dynamic pricing, and model monitoring frameworks to detect feature drift.

Example questions or scenarios:

• "How would you design a machine learning system to predict credit default risk for a new micro-lending product?"
• "Explain how you would monitor a deployed churn prediction model to ensure its accuracy does not degrade."

Leadership and Solution Delivery

This area assesses your ability to drive projects to completion, influence stakeholders, and lead cross-functional initiatives.

Be ready to go over:

• Stakeholder Management – Translating technical findings into clear business recommendations.
• Project Lifecycle Management – Scoping data science projects, defining key milestones, and managing delivery risks.
• Mentorship and Collaboration – Guiding junior team members and collaborating with engineering and product teams.
• Advanced concepts (less common) – Managing external vendor relationships, defining data governance policies, and aligning data strategy with corporate goals.

Example questions or scenarios:

• "Describe a time when you had to convince a business leader to adopt a data-driven approach instead of relying on intuition."
• "How do you prioritize data science initiatives when multiple business units are competing for your team's resources?"